Starting with importing necessary python Libraries and the Dataset

import pandas as pd
data = pd.read_csv("D:\DataAnalysisProjects-master\Instagram Reach Analysis Using Python\Instagram data.csv", encoding = 'latin1')

data

	Impressions	From Home	From Hashtags	From Explore	From Other	Saves	Comments	Shares	Likes	Profile Visits	Follows	Caption	Hashtags
0	3920	2586	1028	619	56	98	9	5	162	35	2	Here are some of the most important data visua...	#finance #money #business #investing #investme...
1	5394	2727	1838	1174	78	194	7	14	224	48	10	Here are some of the best data science project...	#healthcare #health #covid #data #datascience ...
2	4021	2085	1188	0	533	41	11	1	131	62	12	Learn how to train a machine learning model an...	#data #datascience #dataanalysis #dataanalytic...
3	4528	2700	621	932	73	172	10	7	213	23	8	Here’s how you can write a Python program to d...	#python #pythonprogramming #pythonprojects #py...
4	2518	1704	255	279	37	96	5	4	123	8	0	Plotting annotations while visualizing your da...	#datavisualization #datascience #data #dataana...
...	...	...	...	...	...	...	...	...	...	...	...	...	...
114	13700	5185	3041	5352	77	573	2	38	373	73	80	Here are some of the best data science certifi...	#datascience #datasciencejobs #datasciencetrai...
115	5731	1923	1368	2266	65	135	4	1	148	20	18	Clustering is a machine learning technique use...	#machinelearning #machinelearningalgorithms #d...
116	4139	1133	1538	1367	33	36	0	1	92	34	10	Clustering music genres is a task of grouping ...	#machinelearning #machinelearningalgorithms #d...
117	32695	11815	3147	17414	170	1095	2	75	549	148	214	Here are some of the best data science certifi...	#datascience #datasciencejobs #datasciencetrai...
118	36919	13473	4176	16444	2547	653	5	26	443	611	228	175 Python Projects with Source Code solved an...	#python #pythonprogramming #pythonprojects #py...

119 rows × 13 columns

Check whether this data set contains any null values or not

data.isnull().sum()

Impressions       0
From Home         0
From Hashtags     0
From Explore      0
From Other        0
Saves             0
Comments          0
Shares            0
Likes             0
Profile Visits    0
Follows           0
Caption           0
Hashtags          0
dtype: int64

#Dropping null values if any present in the dataset

data=data.dropna()

Understanding the data types of the column

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 119 entries, 0 to 118
Data columns (total 13 columns):
 #   Column          Non-Null Count  Dtype 
---  ------          --------------  ----- 
 0   Impressions     119 non-null    int64 
 1   From Home       119 non-null    int64 
 2   From Hashtags   119 non-null    int64 
 3   From Explore    119 non-null    int64 
 4   From Other      119 non-null    int64 
 5   Saves           119 non-null    int64 
 6   Comments        119 non-null    int64 
 7   Shares          119 non-null    int64 
 8   Likes           119 non-null    int64 
 9   Profile Visits  119 non-null    int64 
 10  Follows         119 non-null    int64 
 11  Caption         119 non-null    object
 12  Hashtags        119 non-null    object
dtypes: int64(11), object(2)
memory usage: 12.2+ KB

Analyzing the Reach based on the distribution of impression that have been recieved from home

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10, 10))
plt.style.use('fivethirtyeight')
plt.title("Distribution of Impression from Home")
sns.distplot(data['From Home'])
plt.show()

C:\Users\Deep Gayen\AppData\Local\Temp\ipykernel_19260\120578938.py:4: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(data['From Home'])

Analyzing the Reach based on the distribution of impression that have been recieved from Hashtags

plt.figure(figsize=(10,8))
plt.title("Distribution Of Impressions From Hashtags")
sns.distplot(data['From Hashtags'])
plt.show()

C:\Users\Deep Gayen\AppData\Local\Temp\ipykernel_19260\4174687964.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(data['From Hashtags'])

Analyzing the Reach based on the distribution of impression that have been recieved from Explore

plt.figure(figsize=(10,8))
plt.title("Distribution of Impression From Explore")
sns.distplot(data['From Explore'])
plt.show()

C:\Users\Deep Gayen\AppData\Local\Temp\ipykernel_19260\3473073779.py:3: UserWarning: 

`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751

  sns.distplot(data['From Explore'])

Percentage of Impression based on different sources on Instagram depicted using Pie chart

import plotly.express as px
home=data['From Home'].sum()
hashtags=data['From Hashtags'].sum()
explore=data['From Explore'].sum()
other=data['From Other'].sum()

labels=['From Home','From Hashtags','From Explore','From Other']
values=[home,hashtags,explore,other]

fig=px.pie(data,values=values,names=labels,title='Impression on Instagram Post From Various Sources',hole=0.5)
fig.show()

Relationship between the number of likes, Comments,Shares and Post Saves and the number of impressions on the Instagram post using Scatter graph

figure=px.scatter(data_frame = data, x="Impressions", y="Comments", size='Comments', trendline="ols", title="Relationship Between Comments and Impressions")
figure.show()

figure=px.scatter(data_frame = data, x="Impressions", y="Likes", size='Likes', trendline="ols", title="Relationship Between Likes and Impressions")
figure.show()

figure=px.scatter(data_frame = data, x="Impressions", y="Shares", size='Shares', trendline="ols", title="Relationship Between Shares and Impressions")
figure.show()

figure=px.scatter(data_frame = data, x="Impressions", y="Saves", size='Saves', trendline="ols", title="Relationship Between Post Saves and Impressions")
figure.show()

Correlation of all the Columns with Impression column

correlation = data.corr()
print(correlation["Impressions"].sort_values(ascending=False))

Impressions       1.000000
From Explore      0.893607
Follows           0.889363
Likes             0.849835
From Home         0.844698
Saves             0.779231
Profile Visits    0.760981
Shares            0.634675
From Other        0.592960
From Hashtags     0.560760
Comments         -0.028524
Name: Impressions, dtype: float64

C:\Users\Deep Gayen\AppData\Local\Temp\ipykernel_19260\3935629544.py:1: FutureWarning:

The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.

The formula that you can use to calculate conversion rate is (Follows/Profile Visits) * 100. Now let’s have a look at the conversation rate of the Instagram account

conversion_rate = (data["Follows"].sum() / data["Profile Visits"].sum()) * 100
print(conversion_rate)

41.00265604249668

Relationship between the total profile visits and the number of followers gained from all profile visits

figure = px.scatter(data_frame = data, x="Profile Visits",
                    y="Follows", size="Follows", trendline="ols", 
                    title = "Relationship Between Profile Visits and Followers Gained")
figure.show()

Training a machine learning model to predict the reach of an Instagram post

import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import PassiveAggressiveRegressor
x = np.array(data[['Likes', 'Saves', 'Comments', 'Shares', 
                   'Profile Visits', 'Follows']])
y = np.array(data["Impressions"])
xtrain, xtest, ytrain, ytest = train_test_split(x, y, 
                                                test_size=0.2, 
                                                random_state=42)

Predict the reach of an Instagram post using Python

model = PassiveAggressiveRegressor()
model.fit(xtrain, ytrain)
model.score(xtest, ytest)

0.6644712240888775

Now let’s predict the reach of an Instagram post by giving inputs to the machine learning model

# Features = [['Likes','Saves', 'Comments', 'Shares', 'Profile Visits', 'Follows']]
features = np.array([[282.0, 233.0, 4.0, 9.0, 165.0, 54.0]])
model.predict(features)

array([8302.40022757])